Aleksey Boyko will present his Pre FPO which is scheduled for Wednesday, May 21st at noon in Rm. 402. Members of his committee are: Thomas Funkhouser (advisor), Brian Kernighan (reader), Szymon Rusinkiewicz (reader), Adam Finkelstein (non-reader), and Jianxiong Xiao(non-reader). Everyone is invited to attend his talk. The title and abstract are below. Title: "On tools and interfaces for efficient and accurate interactive annotation of static 3D point clouds" Abstract: Collecting massive 3D scans of real world environments has become a common practice for many private companies and government agencies. This data is accurate and rich enough to provide impressive visualizations of these environments. However, to truly tap into the potential that such a precise digital depiction of the world offers, these scans need to be annotated. Existing automatic methods report high accuracies for object localization and segmentation, thus greatly improving the complexity of the task. However, the central task of annotation, proper label assignment to the discovered objects, is still a challenging task for existing systems. The goal of this work is to design an interface that facilitates the process of labeling objects in large natural 3D scenes. Prior efforts in this field span a variety of approaches, from purely manual to automatic, to achieve different levels of success. Manual annotation systems produce near perfect results at a cost of enormous human effort. Automatic methods aim at requiring less human input but achieve much lower accuracy, which in turn requires more human interaction. Machine-aided tools attempt to balance these two extremes, however the necessary level of annotator's effort is rarely considered, while the task of training a model that hopefully achieves higher accuracy still takes the center stage. Noticing that the machines have yet a long way to go to match humans' ability to understand real world, and how prone to fatigue and frustration humans are, a preferable approach is to make user effort a priority in any annotation interface. This dissertation assumes the necessity of the human annotator to confirm labels for all objects in order to ensure correctness and explicitly focuses on the tools and interfaces that streamline and facilitate this process. Taking advantage of the scene continuity of the 3D scan data this work advances in two principal directions. First, annotation of objects in groups is proposed to increase the throughput of the information flow from the user to the machine. Second, the non-essential yet time consuming tasks (e.g., scene navigation, selection decisions) are relayed onto a machine by employing an active learning approach to streamline the annotation process and diminish user fatigue and distraction. After evaluating these two directions, a third hybrid approach is proposed---a group active interface. This method takes advantage of the human ability to understand entire scenes and queries objects in groups that are easy to understand and label together thus further increasing the throughput of the annotation process. Empirical evaluation of this approach on a pre-segmented object data indicates an improvement by a factor of 1.7 in annotation time compared to other methods discussed without loss in accuracy.